ISBN-10:
1584883669
ISBN-13:
9781584883661
Pub. Date:
12/28/2004
Publisher:
Taylor & Francis
Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis Series)

Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis Series)

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Overview

Exploratory data analysis (EDA) was conceived at a time when computers were not widely used, and thus computational ability was rather limited. As computational sophistication has increased, EDA has become an even more powerful process for visualizing and summarizing data before making model assumptions to generate hypotheses, encompassing larger and more complex data sets. There are many resources for those interested in the theory of EDA, but this is the first book to use MATLAB to illustrate the computational aspects of this discipline.

Exploratory Data Analysis with MATLAB presents the methods of EDA from a computational perspective. The authors extensively use MATLAB code and algorithm descriptions to provide state-of-the-art techniques for finding patterns and structure in data. Addressing theory, they also incorporate many annotated references to direct readers to the more theoretical aspects of the methods. The book presents an approach using the basic functions from MATLAB and the MATLAB Statistics Toolbox, in order to be more accessible and enduring. It also contains pseudo-code to enable users of other software packages to implement the algorithms.

This text places the tools needed to implement EDA theory at the fingertips of researchers, applied mathematicians, computer scientists, engineers, and statisticians by using a practical/computational approach.

Product Details

ISBN-13: 9781584883661
Publisher: Taylor & Francis
Publication date: 12/28/2004
Series: Chapman & Hall/CRC Computer Science & Data Analysis Series
Edition description: New Edition
Pages: 424
Product dimensions: 6.12(w) x 9.25(h) x 1.10(d)

Table of Contents

INTRODUCTION TO EXPLORATORY DATA ANALYSIS
Introduction to Exploratory Data Analysis
EDA AS PATTERN DISCOVERY
Dimensionality Reduction - Linear Methods
Dimensionality Reduction - Nonlinear Methods
Data Tours
Finding Clusters
Model-Based Clustering
Smoothing Scatterplots
GRAPHICAL METHODS FOR EDA
Visualizing Clusters
Distribution Shapes
Multivariate Visualization
APPENDIX A: PROXIMITY MEASURES
APPENDIX B: SOFTWARE RESOURCES FOR EDA
APPENDIX C: DESCRIPTION OF DATA SETS
APPENDIX D: INTRODUCTION TO MATLAB
APPENDIX E: MATLAB FUNCTIONS

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